Overview

Brought to you by YData

Dataset statistics

Number of variables14
Number of observations7253
Missing cells7628
Missing cells (%)7.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory793.4 KiB
Average record size in memory112.0 B

Variable types

Numeric5
Text5
Categorical4

Alerts

Kilometers_Driven is highly overall correlated with YearHigh correlation
Price is highly overall correlated with TransmissionHigh correlation
Transmission is highly overall correlated with PriceHigh correlation
Year is highly overall correlated with Kilometers_DrivenHigh correlation
Fuel_Type is highly imbalanced (53.6%) Imbalance
Owner_Type is highly imbalanced (61.0%) Imbalance
New_Price has 6247 (86.1%) missing values Missing
Price has 1234 (17.0%) missing values Missing
Kilometers_Driven is highly skewed (γ1 = 61.58257466) Skewed
S.No. is uniformly distributed Uniform
S.No. has unique values Unique

Reproduction

Analysis started2025-05-27 06:49:21.679651
Analysis finished2025-05-27 06:49:26.335895
Duration4.66 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

S.No.
Real number (ℝ)

Uniform  Unique 

Distinct7253
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3626
Minimum0
Maximum7252
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-05-27T06:49:26.448496image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile362.6
Q11813
median3626
Q35439
95-th percentile6889.4
Maximum7252
Range7252
Interquartile range (IQR)3626

Descriptive statistics

Standard deviation2093.9051
Coefficient of variation (CV)0.57746969
Kurtosis-1.2
Mean3626
Median Absolute Deviation (MAD)1813
Skewness0
Sum26299378
Variance4384438.5
MonotonicityStrictly increasing
2025-05-27T06:49:26.584617image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7252 1
 
< 0.1%
0 1
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
7236 1
 
< 0.1%
7235 1
 
< 0.1%
7234 1
 
< 0.1%
7233 1
 
< 0.1%
7232 1
 
< 0.1%
Other values (7243) 7243
99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
7252 1
< 0.1%
7251 1
< 0.1%
7250 1
< 0.1%
7249 1
< 0.1%
7248 1
< 0.1%
7247 1
< 0.1%
7246 1
< 0.1%
7245 1
< 0.1%
7244 1
< 0.1%
7243 1
< 0.1%

Name
Text

Distinct2041
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
2025-05-27T06:49:27.218853image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length59
Median length48
Mean length26.15759
Min length11

Characters and Unicode

Total characters189721
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique862 ?
Unique (%)11.9%

Sample

1st rowMaruti Wagon R LXI CNG
2nd rowHyundai Creta 1.6 CRDi SX Option
3rd rowHonda Jazz V
4th rowMaruti Ertiga VDI
5th rowAudi A4 New 2.0 TDI Multitronic
ValueCountFrequency (%)
maruti 1444
 
4.1%
hyundai 1340
 
3.8%
honda 743
 
2.1%
at 655
 
1.9%
diesel 609
 
1.7%
1.2 521
 
1.5%
toyota 507
 
1.4%
tdi 479
 
1.4%
mt 419
 
1.2%
swift 418
 
1.2%
Other values (907) 27958
79.7%
2025-05-27T06:49:27.784781image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
27840
 
14.7%
a 11809
 
6.2%
i 11426
 
6.0%
e 9233
 
4.9%
t 7952
 
4.2%
o 7950
 
4.2%
n 7822
 
4.1%
r 7647
 
4.0%
u 5214
 
2.7%
d 4930
 
2.6%
Other values (59) 87898
46.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 189721
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
27840
 
14.7%
a 11809
 
6.2%
i 11426
 
6.0%
e 9233
 
4.9%
t 7952
 
4.2%
o 7950
 
4.2%
n 7822
 
4.1%
r 7647
 
4.0%
u 5214
 
2.7%
d 4930
 
2.6%
Other values (59) 87898
46.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 189721
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
27840
 
14.7%
a 11809
 
6.2%
i 11426
 
6.0%
e 9233
 
4.9%
t 7952
 
4.2%
o 7950
 
4.2%
n 7822
 
4.1%
r 7647
 
4.0%
u 5214
 
2.7%
d 4930
 
2.6%
Other values (59) 87898
46.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 189721
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
27840
 
14.7%
a 11809
 
6.2%
i 11426
 
6.0%
e 9233
 
4.9%
t 7952
 
4.2%
o 7950
 
4.2%
n 7822
 
4.1%
r 7647
 
4.0%
u 5214
 
2.7%
d 4930
 
2.6%
Other values (59) 87898
46.3%

Location
Categorical

Distinct11
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
Mumbai
949 
Hyderabad
876 
Coimbatore
772 
Kochi
772 
Pune
765 
Other values (6)
3119 

Length

Max length10
Median length7
Mean length6.8470978
Min length4

Characters and Unicode

Total characters49662
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMumbai
2nd rowPune
3rd rowChennai
4th rowChennai
5th rowCoimbatore

Common Values

ValueCountFrequency (%)
Mumbai 949
13.1%
Hyderabad 876
12.1%
Coimbatore 772
10.6%
Kochi 772
10.6%
Pune 765
10.5%
Delhi 660
9.1%
Kolkata 654
9.0%
Chennai 591
8.1%
Jaipur 499
6.9%
Bangalore 440
6.1%

Length

2025-05-27T06:49:27.909098image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
mumbai 949
13.1%
hyderabad 876
12.1%
coimbatore 772
10.6%
kochi 772
10.6%
pune 765
10.5%
delhi 660
9.1%
kolkata 654
9.0%
chennai 591
8.1%
jaipur 499
6.9%
bangalore 440
6.1%

Most occurring characters

ValueCountFrequency (%)
a 7301
14.7%
e 4379
 
8.8%
i 4243
 
8.5%
o 3410
 
6.9%
b 2872
 
5.8%
r 2587
 
5.2%
n 2387
 
4.8%
d 2302
 
4.6%
h 2298
 
4.6%
u 2213
 
4.5%
Other values (17) 15670
31.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49662
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7301
14.7%
e 4379
 
8.8%
i 4243
 
8.5%
o 3410
 
6.9%
b 2872
 
5.8%
r 2587
 
5.2%
n 2387
 
4.8%
d 2302
 
4.6%
h 2298
 
4.6%
u 2213
 
4.5%
Other values (17) 15670
31.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49662
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7301
14.7%
e 4379
 
8.8%
i 4243
 
8.5%
o 3410
 
6.9%
b 2872
 
5.8%
r 2587
 
5.2%
n 2387
 
4.8%
d 2302
 
4.6%
h 2298
 
4.6%
u 2213
 
4.5%
Other values (17) 15670
31.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49662
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7301
14.7%
e 4379
 
8.8%
i 4243
 
8.5%
o 3410
 
6.9%
b 2872
 
5.8%
r 2587
 
5.2%
n 2387
 
4.8%
d 2302
 
4.6%
h 2298
 
4.6%
u 2213
 
4.5%
Other values (17) 15670
31.6%

Year
Real number (ℝ)

High correlation 

Distinct23
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2013.3654
Minimum1996
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-05-27T06:49:28.012980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1996
5-th percentile2007
Q12011
median2014
Q32016
95-th percentile2018
Maximum2019
Range23
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2544208
Coefficient of variation (CV)0.0016164085
Kurtosis0.91048764
Mean2013.3654
Median Absolute Deviation (MAD)2
Skewness-0.83981615
Sum14602939
Variance10.591255
MonotonicityNot monotonic
2025-05-27T06:49:28.131512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2015 929
12.8%
2014 925
12.8%
2016 886
12.2%
2013 791
10.9%
2017 709
9.8%
2012 690
9.5%
2011 579
8.0%
2010 407
5.6%
2018 361
 
5.0%
2009 252
 
3.5%
Other values (13) 724
10.0%
ValueCountFrequency (%)
1996 1
 
< 0.1%
1998 4
 
0.1%
1999 2
 
< 0.1%
2000 5
 
0.1%
2001 8
 
0.1%
2002 18
 
0.2%
2003 20
 
0.3%
2004 35
 
0.5%
2005 68
0.9%
2006 89
1.2%
ValueCountFrequency (%)
2019 119
 
1.6%
2018 361
 
5.0%
2017 709
9.8%
2016 886
12.2%
2015 929
12.8%
2014 925
12.8%
2013 791
10.9%
2012 690
9.5%
2011 579
8.0%
2010 407
5.6%

Kilometers_Driven
Real number (ℝ)

High correlation  Skewed 

Distinct3660
Distinct (%)50.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58699.063
Minimum171
Maximum6500000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-05-27T06:49:28.255930image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum171
5-th percentile13017.6
Q134000
median53416
Q373000
95-th percentile120241.8
Maximum6500000
Range6499829
Interquartile range (IQR)39000

Descriptive statistics

Standard deviation84427.721
Coefficient of variation (CV)1.4383146
Kurtosis4674.734
Mean58699.063
Median Absolute Deviation (MAD)19584
Skewness61.582575
Sum4.257443 × 108
Variance7.12804 × 109
MonotonicityNot monotonic
2025-05-27T06:49:28.395916image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60000 96
 
1.3%
65000 86
 
1.2%
45000 86
 
1.2%
70000 77
 
1.1%
50000 72
 
1.0%
55000 66
 
0.9%
75000 64
 
0.9%
35000 62
 
0.9%
52000 62
 
0.9%
80000 62
 
0.9%
Other values (3650) 6520
89.9%
ValueCountFrequency (%)
171 1
 
< 0.1%
600 1
 
< 0.1%
1000 11
0.2%
1001 4
 
0.1%
1011 1
 
< 0.1%
1015 1
 
< 0.1%
1048 1
 
< 0.1%
1261 1
 
< 0.1%
1331 1
 
< 0.1%
1400 1
 
< 0.1%
ValueCountFrequency (%)
6500000 1
< 0.1%
775000 1
< 0.1%
720000 1
< 0.1%
620000 1
< 0.1%
480000 2
< 0.1%
445000 1
< 0.1%
350000 1
< 0.1%
300000 1
< 0.1%
299322 1
< 0.1%
290000 1
< 0.1%

Fuel_Type
Categorical

Imbalance 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
Diesel
3852 
Petrol
3325 
CNG
 
62
LPG
 
12
Electric
 
2

Length

Max length8
Median length6
Mean length5.9699435
Min length3

Characters and Unicode

Total characters43300
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowCNG
2nd rowDiesel
3rd rowPetrol
4th rowDiesel
5th rowDiesel

Common Values

ValueCountFrequency (%)
Diesel 3852
53.1%
Petrol 3325
45.8%
CNG 62
 
0.9%
LPG 12
 
0.2%
Electric 2
 
< 0.1%

Length

2025-05-27T06:49:28.531275image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T06:49:28.626774image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
diesel 3852
53.1%
petrol 3325
45.8%
cng 62
 
0.9%
lpg 12
 
0.2%
electric 2
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 11031
25.5%
l 7179
16.6%
i 3854
 
8.9%
D 3852
 
8.9%
s 3852
 
8.9%
P 3337
 
7.7%
t 3327
 
7.7%
r 3327
 
7.7%
o 3325
 
7.7%
G 74
 
0.2%
Other values (5) 142
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43300
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 11031
25.5%
l 7179
16.6%
i 3854
 
8.9%
D 3852
 
8.9%
s 3852
 
8.9%
P 3337
 
7.7%
t 3327
 
7.7%
r 3327
 
7.7%
o 3325
 
7.7%
G 74
 
0.2%
Other values (5) 142
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43300
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 11031
25.5%
l 7179
16.6%
i 3854
 
8.9%
D 3852
 
8.9%
s 3852
 
8.9%
P 3337
 
7.7%
t 3327
 
7.7%
r 3327
 
7.7%
o 3325
 
7.7%
G 74
 
0.2%
Other values (5) 142
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43300
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 11031
25.5%
l 7179
16.6%
i 3854
 
8.9%
D 3852
 
8.9%
s 3852
 
8.9%
P 3337
 
7.7%
t 3327
 
7.7%
r 3327
 
7.7%
o 3325
 
7.7%
G 74
 
0.2%
Other values (5) 142
 
0.3%

Transmission
Categorical

High correlation 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
Manual
5204 
Automatic
2049 

Length

Max length9
Median length6
Mean length6.8475114
Min length6

Characters and Unicode

Total characters49665
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowManual
2nd rowManual
3rd rowManual
4th rowManual
5th rowAutomatic

Common Values

ValueCountFrequency (%)
Manual 5204
71.7%
Automatic 2049
 
28.3%

Length

2025-05-27T06:49:28.746453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T06:49:28.817347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
manual 5204
71.7%
automatic 2049
 
28.3%

Most occurring characters

ValueCountFrequency (%)
a 12457
25.1%
u 7253
14.6%
M 5204
10.5%
n 5204
10.5%
l 5204
10.5%
t 4098
 
8.3%
A 2049
 
4.1%
o 2049
 
4.1%
m 2049
 
4.1%
i 2049
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49665
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 12457
25.1%
u 7253
14.6%
M 5204
10.5%
n 5204
10.5%
l 5204
10.5%
t 4098
 
8.3%
A 2049
 
4.1%
o 2049
 
4.1%
m 2049
 
4.1%
i 2049
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49665
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 12457
25.1%
u 7253
14.6%
M 5204
10.5%
n 5204
10.5%
l 5204
10.5%
t 4098
 
8.3%
A 2049
 
4.1%
o 2049
 
4.1%
m 2049
 
4.1%
i 2049
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49665
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 12457
25.1%
u 7253
14.6%
M 5204
10.5%
n 5204
10.5%
l 5204
10.5%
t 4098
 
8.3%
A 2049
 
4.1%
o 2049
 
4.1%
m 2049
 
4.1%
i 2049
 
4.1%

Owner_Type
Categorical

Imbalance 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.8 KiB
First
5952 
Second
1152 
Third
 
137
Fourth & Above
 
12

Length

Max length14
Median length5
Mean length5.1737212
Min length5

Characters and Unicode

Total characters37525
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFirst
2nd rowFirst
3rd rowFirst
4th rowFirst
5th rowSecond

Common Values

ValueCountFrequency (%)
First 5952
82.1%
Second 1152
 
15.9%
Third 137
 
1.9%
Fourth & Above 12
 
0.2%

Length

2025-05-27T06:49:28.909995image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-27T06:49:28.987072image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
first 5952
81.8%
second 1152
 
15.8%
third 137
 
1.9%
fourth 12
 
0.2%
12
 
0.2%
above 12
 
0.2%

Most occurring characters

ValueCountFrequency (%)
r 6101
16.3%
i 6089
16.2%
F 5964
15.9%
t 5964
15.9%
s 5952
15.9%
d 1289
 
3.4%
o 1176
 
3.1%
e 1164
 
3.1%
S 1152
 
3.1%
c 1152
 
3.1%
Other values (9) 1522
 
4.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 37525
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 6101
16.3%
i 6089
16.2%
F 5964
15.9%
t 5964
15.9%
s 5952
15.9%
d 1289
 
3.4%
o 1176
 
3.1%
e 1164
 
3.1%
S 1152
 
3.1%
c 1152
 
3.1%
Other values (9) 1522
 
4.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 37525
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 6101
16.3%
i 6089
16.2%
F 5964
15.9%
t 5964
15.9%
s 5952
15.9%
d 1289
 
3.4%
o 1176
 
3.1%
e 1164
 
3.1%
S 1152
 
3.1%
c 1152
 
3.1%
Other values (9) 1522
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 37525
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 6101
16.3%
i 6089
16.2%
F 5964
15.9%
t 5964
15.9%
s 5952
15.9%
d 1289
 
3.4%
o 1176
 
3.1%
e 1164
 
3.1%
S 1152
 
3.1%
c 1152
 
3.1%
Other values (9) 1522
 
4.1%

Mileage
Text

Distinct450
Distinct (%)6.2%
Missing2
Missing (%)< 0.1%
Memory size56.8 KiB
2025-05-27T06:49:29.378085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length9
Mean length9.3970487
Min length8

Characters and Unicode

Total characters68138
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)0.8%

Sample

1st row26.6 km/kg
2nd row19.67 kmpl
3rd row18.2 kmpl
4th row20.77 kmpl
5th row15.2 kmpl
ValueCountFrequency (%)
kmpl 7177
49.5%
17.0 208
 
1.4%
18.9 201
 
1.4%
18.6 144
 
1.0%
21.1 107
 
0.7%
20.36 105
 
0.7%
17.8 98
 
0.7%
18.0 89
 
0.6%
12.8 87
 
0.6%
18.5 86
 
0.6%
Other values (430) 6200
42.8%
2025-05-27T06:49:29.906384image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
k 7325
10.8%
. 7251
10.6%
7251
10.6%
m 7251
10.6%
l 7177
10.5%
p 7177
10.5%
1 6371
9.4%
2 3941
 
5.8%
0 2224
 
3.3%
7 1968
 
2.9%
Other values (8) 10202
15.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 68138
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
k 7325
10.8%
. 7251
10.6%
7251
10.6%
m 7251
10.6%
l 7177
10.5%
p 7177
10.5%
1 6371
9.4%
2 3941
 
5.8%
0 2224
 
3.3%
7 1968
 
2.9%
Other values (8) 10202
15.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 68138
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
k 7325
10.8%
. 7251
10.6%
7251
10.6%
m 7251
10.6%
l 7177
10.5%
p 7177
10.5%
1 6371
9.4%
2 3941
 
5.8%
0 2224
 
3.3%
7 1968
 
2.9%
Other values (8) 10202
15.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 68138
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
k 7325
10.8%
. 7251
10.6%
7251
10.6%
m 7251
10.6%
l 7177
10.5%
p 7177
10.5%
1 6371
9.4%
2 3941
 
5.8%
0 2224
 
3.3%
7 1968
 
2.9%
Other values (8) 10202
15.0%

Engine
Text

Distinct150
Distinct (%)2.1%
Missing46
Missing (%)0.6%
Memory size56.8 KiB
2025-05-27T06:49:30.311035image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.9006521
Min length5

Characters and Unicode

Total characters49733
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.3%

Sample

1st row998 CC
2nd row1582 CC
3rd row1199 CC
4th row1248 CC
5th row1968 CC
ValueCountFrequency (%)
cc 7207
50.0%
1197 732
 
5.1%
1248 610
 
4.2%
1498 370
 
2.6%
998 309
 
2.1%
1198 281
 
1.9%
2179 278
 
1.9%
1497 273
 
1.9%
1968 266
 
1.8%
1995 212
 
1.5%
Other values (141) 3876
26.9%
2025-05-27T06:49:30.867031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 14414
29.0%
1 7427
14.9%
7207
14.5%
9 6571
13.2%
8 3014
 
6.1%
4 2585
 
5.2%
2 2461
 
4.9%
7 2056
 
4.1%
6 1468
 
3.0%
3 1144
 
2.3%
Other values (2) 1386
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 49733
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
C 14414
29.0%
1 7427
14.9%
7207
14.5%
9 6571
13.2%
8 3014
 
6.1%
4 2585
 
5.2%
2 2461
 
4.9%
7 2056
 
4.1%
6 1468
 
3.0%
3 1144
 
2.3%
Other values (2) 1386
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 49733
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
C 14414
29.0%
1 7427
14.9%
7207
14.5%
9 6571
13.2%
8 3014
 
6.1%
4 2585
 
5.2%
2 2461
 
4.9%
7 2056
 
4.1%
6 1468
 
3.0%
3 1144
 
2.3%
Other values (2) 1386
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 49733
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
C 14414
29.0%
1 7427
14.9%
7207
14.5%
9 6571
13.2%
8 3014
 
6.1%
4 2585
 
5.2%
2 2461
 
4.9%
7 2056
 
4.1%
6 1468
 
3.0%
3 1144
 
2.3%
Other values (2) 1386
 
2.8%

Power
Text

Distinct386
Distinct (%)5.4%
Missing46
Missing (%)0.6%
Memory size56.8 KiB
2025-05-27T06:49:31.405933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length11
Median length10
Mean length7.9353406
Min length6

Characters and Unicode

Total characters57190
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)0.9%

Sample

1st row58.16 bhp
2nd row126.2 bhp
3rd row88.7 bhp
4th row88.76 bhp
5th row140.8 bhp
ValueCountFrequency (%)
bhp 7207
50.0%
74 280
 
1.9%
98.6 166
 
1.2%
73.9 152
 
1.1%
140 142
 
1.0%
null 129
 
0.9%
78.9 128
 
0.9%
67.1 126
 
0.9%
67.04 125
 
0.9%
82 124
 
0.9%
Other values (377) 5835
40.5%
2025-05-27T06:49:32.182194image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
b 7207
12.6%
7207
12.6%
h 7207
12.6%
p 7207
12.6%
. 4464
7.8%
1 4411
7.7%
8 3840
6.7%
7 2779
 
4.9%
3 2129
 
3.7%
6 2079
 
3.6%
Other values (8) 8660
15.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 57190
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
b 7207
12.6%
7207
12.6%
h 7207
12.6%
p 7207
12.6%
. 4464
7.8%
1 4411
7.7%
8 3840
6.7%
7 2779
 
4.9%
3 2129
 
3.7%
6 2079
 
3.6%
Other values (8) 8660
15.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 57190
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
b 7207
12.6%
7207
12.6%
h 7207
12.6%
p 7207
12.6%
. 4464
7.8%
1 4411
7.7%
8 3840
6.7%
7 2779
 
4.9%
3 2129
 
3.7%
6 2079
 
3.6%
Other values (8) 8660
15.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 57190
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
b 7207
12.6%
7207
12.6%
h 7207
12.6%
p 7207
12.6%
. 4464
7.8%
1 4411
7.7%
8 3840
6.7%
7 2779
 
4.9%
3 2129
 
3.7%
6 2079
 
3.6%
Other values (8) 8660
15.1%

Seats
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing53
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean5.2797222
Minimum0
Maximum10
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-05-27T06:49:32.414522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q15
median5
Q35
95-th percentile7
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.81165967
Coefficient of variation (CV)0.15373151
Kurtosis4.7034289
Mean5.2797222
Median Absolute Deviation (MAD)0
Skewness1.902262
Sum38014
Variance0.65879142
MonotonicityNot monotonic
2025-05-27T06:49:32.552775image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5 6047
83.4%
7 796
 
11.0%
8 170
 
2.3%
4 119
 
1.6%
6 38
 
0.5%
2 18
 
0.2%
10 8
 
0.1%
9 3
 
< 0.1%
0 1
 
< 0.1%
(Missing) 53
 
0.7%
ValueCountFrequency (%)
0 1
 
< 0.1%
2 18
 
0.2%
4 119
 
1.6%
5 6047
83.4%
6 38
 
0.5%
7 796
 
11.0%
8 170
 
2.3%
9 3
 
< 0.1%
10 8
 
0.1%
ValueCountFrequency (%)
10 8
 
0.1%
9 3
 
< 0.1%
8 170
 
2.3%
7 796
 
11.0%
6 38
 
0.5%
5 6047
83.4%
4 119
 
1.6%
2 18
 
0.2%
0 1
 
< 0.1%

New_Price
Text

Missing 

Distinct625
Distinct (%)62.1%
Missing6247
Missing (%)86.1%
Memory size56.8 KiB
2025-05-27T06:49:32.932854image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.4194831
Min length4

Characters and Unicode

Total characters9476
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique391 ?
Unique (%)38.9%

Sample

1st row8.61 Lakh
2nd row21 Lakh
3rd row10.65 Lakh
4th row32.01 Lakh
5th row47.87 Lakh
ValueCountFrequency (%)
lakh 986
49.0%
cr 20
 
1.0%
95.13 6
 
0.3%
63.71 6
 
0.3%
33.36 6
 
0.3%
4.78 6
 
0.3%
5.04 5
 
0.2%
15.05 5
 
0.2%
4.09 5
 
0.2%
9.12 5
 
0.2%
Other values (617) 962
47.8%
2025-05-27T06:49:33.442294image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1006
10.6%
. 989
10.4%
h 986
10.4%
L 986
10.4%
k 986
10.4%
a 986
10.4%
1 627
 
6.6%
4 386
 
4.1%
5 371
 
3.9%
7 356
 
3.8%
Other values (8) 1797
19.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9476
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1006
10.6%
. 989
10.4%
h 986
10.4%
L 986
10.4%
k 986
10.4%
a 986
10.4%
1 627
 
6.6%
4 386
 
4.1%
5 371
 
3.9%
7 356
 
3.8%
Other values (8) 1797
19.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9476
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1006
10.6%
. 989
10.4%
h 986
10.4%
L 986
10.4%
k 986
10.4%
a 986
10.4%
1 627
 
6.6%
4 386
 
4.1%
5 371
 
3.9%
7 356
 
3.8%
Other values (8) 1797
19.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9476
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1006
10.6%
. 989
10.4%
h 986
10.4%
L 986
10.4%
k 986
10.4%
a 986
10.4%
1 627
 
6.6%
4 386
 
4.1%
5 371
 
3.9%
7 356
 
3.8%
Other values (8) 1797
19.0%

Price
Real number (ℝ)

High correlation  Missing 

Distinct1373
Distinct (%)22.8%
Missing1234
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean9.4794684
Minimum0.44
Maximum160
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size56.8 KiB
2025-05-27T06:49:33.579190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.44
5-th percentile1.7
Q13.5
median5.64
Q39.95
95-th percentile32.446
Maximum160
Range159.56
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation11.187917
Coefficient of variation (CV)1.1802262
Kurtosis17.092202
Mean9.4794684
Median Absolute Deviation (MAD)2.62
Skewness3.335232
Sum57056.92
Variance125.16949
MonotonicityNot monotonic
2025-05-27T06:49:33.718411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.5 88
 
1.2%
5.5 84
 
1.2%
3.5 82
 
1.1%
4.25 73
 
1.0%
3.25 71
 
1.0%
3 68
 
0.9%
6.5 64
 
0.9%
2.5 63
 
0.9%
4 56
 
0.8%
4.75 53
 
0.7%
Other values (1363) 5317
73.3%
(Missing) 1234
 
17.0%
ValueCountFrequency (%)
0.44 1
 
< 0.1%
0.45 3
< 0.1%
0.5 2
< 0.1%
0.51 1
 
< 0.1%
0.53 2
< 0.1%
0.55 3
< 0.1%
0.6 2
< 0.1%
0.63 1
 
< 0.1%
0.65 2
< 0.1%
0.69 1
 
< 0.1%
ValueCountFrequency (%)
160 1
< 0.1%
120 1
< 0.1%
100 1
< 0.1%
97.07 1
< 0.1%
93.67 1
< 0.1%
93 1
< 0.1%
90 1
< 0.1%
85 1
< 0.1%
83.96 1
< 0.1%
79 2
< 0.1%

Interactions

2025-05-27T06:49:25.114220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:22.607648image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:23.193085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:23.945554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:24.537593image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:25.233849image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:22.737077image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:23.308153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:24.063031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:24.655201image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:25.361610image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:22.859353image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:23.409607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:24.174042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:24.766575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:25.488553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:22.971546image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:23.519615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:24.291433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:24.879158image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:25.603104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:23.072525image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:23.811358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:24.400347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-27T06:49:25.000653image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-27T06:49:33.825584image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Fuel_TypeKilometers_DrivenLocationOwner_TypePriceS.No.SeatsTransmissionYear
Fuel_Type1.0000.0000.0930.0230.1480.0190.1600.1500.095
Kilometers_Driven0.0001.0000.0080.000-0.2150.0040.1910.012-0.552
Location0.0930.0081.0000.1600.0740.0000.0260.1950.136
Owner_Type0.0230.0000.1601.0000.0580.0000.0460.0070.251
Price0.148-0.2150.0740.0581.000-0.0070.2210.5950.491
S.No.0.0190.0040.0000.000-0.0071.000-0.0090.0000.008
Seats0.1600.1910.0260.0460.221-0.0091.0000.1460.031
Transmission0.1500.0120.1950.0070.5950.0000.1461.0000.101
Year0.095-0.5520.1360.2510.4910.0080.0310.1011.000

Missing values

2025-05-27T06:49:25.795912image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-27T06:49:25.979439image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-05-27T06:49:26.225733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

S.No.NameLocationYearKilometers_DrivenFuel_TypeTransmissionOwner_TypeMileageEnginePowerSeatsNew_PricePrice
00Maruti Wagon R LXI CNGMumbai201072000CNGManualFirst26.6 km/kg998 CC58.16 bhp5.0NaN1.75
11Hyundai Creta 1.6 CRDi SX OptionPune201541000DieselManualFirst19.67 kmpl1582 CC126.2 bhp5.0NaN12.50
22Honda Jazz VChennai201146000PetrolManualFirst18.2 kmpl1199 CC88.7 bhp5.08.61 Lakh4.50
33Maruti Ertiga VDIChennai201287000DieselManualFirst20.77 kmpl1248 CC88.76 bhp7.0NaN6.00
44Audi A4 New 2.0 TDI MultitronicCoimbatore201340670DieselAutomaticSecond15.2 kmpl1968 CC140.8 bhp5.0NaN17.74
55Hyundai EON LPG Era Plus OptionHyderabad201275000LPGManualFirst21.1 km/kg814 CC55.2 bhp5.0NaN2.35
66Nissan Micra Diesel XVJaipur201386999DieselManualFirst23.08 kmpl1461 CC63.1 bhp5.0NaN3.50
77Toyota Innova Crysta 2.8 GX AT 8SMumbai201636000DieselAutomaticFirst11.36 kmpl2755 CC171.5 bhp8.021 Lakh17.50
88Volkswagen Vento Diesel ComfortlinePune201364430DieselManualFirst20.54 kmpl1598 CC103.6 bhp5.0NaN5.20
99Tata Indica Vista Quadrajet LSChennai201265932DieselManualSecond22.3 kmpl1248 CC74 bhp5.0NaN1.95
S.No.NameLocationYearKilometers_DrivenFuel_TypeTransmissionOwner_TypeMileageEnginePowerSeatsNew_PricePrice
72437243Renault Duster 85PS Diesel RxLChennai201570000DieselManualFirst19.87 kmpl1461 CC83.8 bhp5.0NaNNaN
72447244Chevrolet Aveo 1.4 LSPune200945463PetrolManualFirst14.49 kmpl1399 CC92.7 bhp5.0NaNNaN
72457245Honda Amaze S i-VtechKochi201544776PetrolManualFirst18.0 kmpl1198 CC86.7 bhp5.0NaNNaN
72467246Hyundai Grand i10 AT AstaCoimbatore201618242PetrolAutomaticFirst18.9 kmpl1197 CC82 bhp5.0NaNNaN
72477247Hyundai EON D Lite PlusCoimbatore201521190PetrolManualFirst21.1 kmpl814 CC55.2 bhp5.0NaNNaN
72487248Volkswagen Vento Diesel TrendlineHyderabad201189411DieselManualFirst20.54 kmpl1598 CC103.6 bhp5.0NaNNaN
72497249Volkswagen Polo GT TSIMumbai201559000PetrolAutomaticFirst17.21 kmpl1197 CC103.6 bhp5.0NaNNaN
72507250Nissan Micra Diesel XVKolkata201228000DieselManualFirst23.08 kmpl1461 CC63.1 bhp5.0NaNNaN
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